What are popular: Exploring twitter features for event detection, tracking and visualization

Cai, Hongyun, Yang, Yang, Li, Xuefei and Huang, Zi (2015). What are popular: Exploring twitter features for event detection, tracking and visualization. In: MM 2015 - Proceedings of the 23rd ACM Multimedia Conference. 23rd ACM International Conference on Multimedia, MM 2015, Brisbane, QLD, Australia, (89-98). 26-30 October, 2015. doi:10.1145/2733373.2806236


Author Cai, Hongyun
Yang, Yang
Li, Xuefei
Huang, Zi
Title of paper What are popular: Exploring twitter features for event detection, tracking and visualization
Conference name 23rd ACM International Conference on Multimedia, MM 2015
Conference location Brisbane, QLD, Australia
Conference dates 26-30 October, 2015
Convener ACM
Proceedings title MM 2015 - Proceedings of the 23rd ACM Multimedia Conference
Journal name MM 2015 - Proceedings of the 2015 ACM Multimedia Conference
Series Proceedings of the ACM Multimedia Conference
Place of Publication New York , NY, United States
Publisher Association for Computing Machinery
Publication Year 2015
Sub-type Fully published paper
DOI 10.1145/2733373.2806236
Open Access Status Not Open Access
ISBN 9781450334594
Volume 23
Start page 89
End page 98
Total pages 10
Collection year 2016
Language eng
Abstract/Summary As one of the most representative social media platforms, Twitter provides various real-life information on social events in real time. Despite that social event detection has been actively studied, tweet images, which appear in around 36 percent of the total tweets, have not been well utilized for this research problem. Most existing event detection methods tend to represent an image as a bag-of-visual-words and then process these visual words in the same way as textual words. This may not fully exploit the visual properties of images. State-of-the-art visual features like convolutional neural network (CNN) features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the image's semantics. Unfortunately, they have not been employed in detecting events from social websites. Hence, how to make the most of tweet images to improve the performance of social event detection and visualization remains open. In this paper, we thoroughly study the impact of tweet images on social event detection for different event categories using various visual features. A novel topic model which jointly models five Twitter features (text, image, location, timestamp and hashtag) is designed to discover events from the sheer amount of tweets. Moreover, the evolutions of events are tracked by linking the events detected on adjacent days and each event is visualized by representative images selected on three predefined criteria. Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.
Keyword Event Tracking
Event Visualization
Twitter Event Detection
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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